Chapter 1, Ensemble Methods for Regression and Classification, covers the application of ensemble methods or algorithms to produce accurate predictions of models. We will go through the application of ensemble methods for regression and classification problems.
Chapter 2, Cross-validation and Parameter Tuning, explores various techniques to combine and build better models. We will learn different methods of cross-validation, including holdout cross-validation and k-fold cross-validation. We will also discuss what hyperparameter tuning is.
Chapter 3, Working with Features, explores feature selection methods, dimensionality reduction, PCA, and feature engineering. We will also study methods to improve models with feature engineering.
Chapter 4, Introduction to Artificial Neural Networks and TensorFlow, is an introduction to ANNs and TensorFlow. We will explore the various elements in the network and their functions. We will also learn the basic concepts of TensorFlow in it.
Chapter 5, Predictive Analytics with TensorFlow and Deep Neural Networks, explores predictive analytics with the help of TensorFlow and deep learning. We will study the MNIST dataset and classification of models using this dataset. We will learn about DNNs, their functions, and the application of DNNs to the MNIST dataset.
